QTM 385 - Experimental Methods

Lecture 16 - Interference and Spillovers

Danilo Freire

Emory University

How are you doing? 😊

Natural experiments framework:
- True vs “as-if” randomness in treatment assignment
- Core assumption: exogeneity of assignment mechanism
- Examples: Lottery-based charter school studies, border discontinuity designs
Quasi-experimental approaches:
- Regression discontinuity (RDD): Leveraging threshold-based assignment
- Difference-in-differences (DID): Utilising parallel trends assumption
Methodological challenges:
- Selection bias in observational data
- SUTVA violations from treatment spillovers
- Power limitations in natural variation contexts

Empirical examples:
- Angrist et al. (2013): School lottery IV analysis
- Card & Krueger (1994): Minimum wage DID study
- Mignozzetti et al. (2024): RDD in legislative analysis
Validation strategies:
- Placebo tests for assumption verification
- Pre-treatment trend analysis for DID
- Robustness checks for sensitivity assessments
Ethical considerations:
- Responsible communication of limitations
- Secondary data ethics compliance
- Policy impact assessments for natural experiments

Remember SUTVA?
- Stable unit treatment value assumption
The stable part means that potential outcomes should be independent of the treatment
As you can imagine, this poses risks to causal identification
This happens quite often in social and public health interventions:
- Peer effects in education
- Contagion in public health
- Spillovers in policy evaluations
- Network effects in marketing and technology